Bibliographic record
Abstract
For many members of marginalized communities, law is all too often an author of oppression, and the justice system is a site not of justice but of threat and harm. Yet most access to justice projects in Canada devote themselves to the task of rendering law and the justice system more available to the public without a serious consideration of these critical and troubling community-held insights. In this article, I draw on qualitative interviews conducted with community members in Saskatoon and the literature on law’s violence to argue that those who are concerned about access to justice must come to terms with harms done through law and legal processes upon members of marginalized communities. This requires a commitment by those working within the justice system to take seriously the perspectives and experiences of members of marginalized communities who are affected by law and justice systems, and to engage non-defensively with these insights. I argue that this engagement may lead to new ways of thinking about, and engaging with, access to justice. Specifically, I propose that it may lead to a de-emphasis on the current focus on “access” and a renewed emphasis on learning with and from communities about what it would take to move towards justice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.053 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".